Addition, subtraction, network power of diffnet and logical operators such as & and | as objects
## S3 method for class 'diffnet'x ^ y
graph_power(x, y, valued = getOption("diffnet.valued",FALSE))## S3 method for class 'diffnet'y / x
## S3 method for class 'diffnet'x - y
## S3 method for class 'diffnet'x * y
## S3 method for class 'diffnet'x & y
## S3 method for class 'diffnet'x | y
Arguments
x: A diffnet class object.
y: Integer scalar. Power of the network
valued: Logical scalar. When FALSE all non-zero entries of the adjacency matrices are set to one.
Returns
A diffnet class object
Details
Using binary operators, ease data management process with diffnet.
By default the binary operator ^ assumes that the graph is valued, hence the power is computed using a weighted edges. Otherwise, if more control is needed, the user can use graph_power instead.
Examples
# Computing two-steps away threshold with the Brazilian farmers data --------data(brfarmersDiffNet)expo1 <- threshold(brfarmersDiffNet)expo2 <- threshold(brfarmersDiffNet^2)# Computing correlationcor(expo1,expo2)# Drawing a qqplotqqplot(expo1, expo2)# Working with inverse ------------------------------------------------------brf2_step <- brfarmersDiffNet^2brf2_step <-1/brf2_step
# Removing the first 3 vertex of medInnovationsDiffnet ----------------------data(medInnovationsDiffNet)# Using a diffnet objectfirst3Diffnet <- medInnovationsDiffNet[1:3,,]medInnovationsDiffNet - first3Diffnet
# Using indexesmedInnovationsDiffNet -1:3# Using idsmedInnovationsDiffNet - as.character(1001:1003)
See Also
Other diffnet methods: %*%(), as.array.diffnet(), c.diffnet(), diffnet-class, diffnet_index, plot.diffnet(), summary.diffnet()